DNA microarray classification with compact single hidden-layer feedForward neural networks

Hieu Trung Huynh; Jung Ja Kim; Yonggwan Won

doi:10.1109/FBIT.2007.30

DNA microarray classification with compact single hidden-layer feedForward neural networks

Hieu Trung Huynh
, Jung Ja Kim
, Yonggwan Won

Healthcare Information

Chonnam National University

Research output: Contribution to conference › Conference paper › peer-review

11 Scopus citations

Abstract

Microarrays have been useful in the diagnosis and treatment due to their abilities to survey a large number of genes quickly and to study samples with small amount. With the development of microarray technology, the prospects for effective and reliable disease diagnosis and management can be significantly improved if the classification performance on microarray data is improved. This paper presents an application of the compact single hidden layer feedforward neural networks (C-SLFNs) trained by an improved extreme learning machine (ELM) algorithm to classify microarray data for cancer diagnosis. Experimental results show that the classification accuracy is higher than those achieved by the SLFNs trained by the original ELM and back-propagation (BP) algorithms, and other popular methods for microarray classification such as Support Vector Machine (SVM) and Fisher Discriminant Analysis (FDA). Moreover, the trained C-SLFNs have a smaller number of hidden units than the SLFNs trained by the original ELM and BP, which results in quick response to new input patterns.

Original language	English
Title of host publication	Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007
Pages	193-198
Number of pages	6
DOIs	https://doi.org/10.1109/FBIT.2007.30
State	Published - 2007
Event	Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007 - Jeju Island, Korea, Republic of Duration: 2007.10.11 → 2007.10.13

Publication series

Name	Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007

Conference

Conference	Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007
Country/Territory	Korea, Republic of
City	Jeju Island
Period	07.10.11 → 07.10.13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Quacquarelli Symonds(QS) Subject Topics

Computer Science & Information Systems
Data Science

Access to Document

10.1109/FBIT.2007.30

Cite this

Huynh, H. T., Kim, J. J., & Won, Y. (2007). DNA microarray classification with compact single hidden-layer feedForward neural networks. In Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007 (pp. 193-198). Article 4524103 (Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007). https://doi.org/10.1109/FBIT.2007.30

Huynh, Hieu Trung ; Kim, Jung Ja ; Won, Yonggwan. / DNA microarray classification with compact single hidden-layer feedForward neural networks. Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007. 2007. pp. 193-198 (Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007).

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title = "DNA microarray classification with compact single hidden-layer feedForward neural networks",

abstract = "Microarrays have been useful in the diagnosis and treatment due to their abilities to survey a large number of genes quickly and to study samples with small amount. With the development of microarray technology, the prospects for effective and reliable disease diagnosis and management can be significantly improved if the classification performance on microarray data is improved. This paper presents an application of the compact single hidden layer feedforward neural networks (C-SLFNs) trained by an improved extreme learning machine (ELM) algorithm to classify microarray data for cancer diagnosis. Experimental results show that the classification accuracy is higher than those achieved by the SLFNs trained by the original ELM and back-propagation (BP) algorithms, and other popular methods for microarray classification such as Support Vector Machine (SVM) and Fisher Discriminant Analysis (FDA). Moreover, the trained C-SLFNs have a smaller number of hidden units than the SLFNs trained by the original ELM and BP, which results in quick response to new input patterns.",

author = "Huynh, \{Hieu Trung\} and Kim, \{Jung Ja\} and Yonggwan Won",

year = "2007",

doi = "10.1109/FBIT.2007.30",

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booktitle = "Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007",

note = "Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007 ; Conference date: 11-10-2007 Through 13-10-2007",

}

Huynh, HT, Kim, JJ & Won, Y 2007, DNA microarray classification with compact single hidden-layer feedForward neural networks. in Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007., 4524103, Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007, pp. 193-198, Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007, Jeju Island, Korea, Republic of, 07.10.11. https://doi.org/10.1109/FBIT.2007.30

DNA microarray classification with compact single hidden-layer feedForward neural networks. / Huynh, Hieu Trung; Kim, Jung Ja; Won, Yonggwan.
Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007. 2007. p. 193-198 4524103 (Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007).

Research output: Contribution to conference › Conference paper › peer-review

TY - GEN

T1 - DNA microarray classification with compact single hidden-layer feedForward neural networks

AU - Huynh, Hieu Trung

AU - Kim, Jung Ja

AU - Won, Yonggwan

PY - 2007

Y1 - 2007

N2 - Microarrays have been useful in the diagnosis and treatment due to their abilities to survey a large number of genes quickly and to study samples with small amount. With the development of microarray technology, the prospects for effective and reliable disease diagnosis and management can be significantly improved if the classification performance on microarray data is improved. This paper presents an application of the compact single hidden layer feedforward neural networks (C-SLFNs) trained by an improved extreme learning machine (ELM) algorithm to classify microarray data for cancer diagnosis. Experimental results show that the classification accuracy is higher than those achieved by the SLFNs trained by the original ELM and back-propagation (BP) algorithms, and other popular methods for microarray classification such as Support Vector Machine (SVM) and Fisher Discriminant Analysis (FDA). Moreover, the trained C-SLFNs have a smaller number of hidden units than the SLFNs trained by the original ELM and BP, which results in quick response to new input patterns.

AB - Microarrays have been useful in the diagnosis and treatment due to their abilities to survey a large number of genes quickly and to study samples with small amount. With the development of microarray technology, the prospects for effective and reliable disease diagnosis and management can be significantly improved if the classification performance on microarray data is improved. This paper presents an application of the compact single hidden layer feedforward neural networks (C-SLFNs) trained by an improved extreme learning machine (ELM) algorithm to classify microarray data for cancer diagnosis. Experimental results show that the classification accuracy is higher than those achieved by the SLFNs trained by the original ELM and back-propagation (BP) algorithms, and other popular methods for microarray classification such as Support Vector Machine (SVM) and Fisher Discriminant Analysis (FDA). Moreover, the trained C-SLFNs have a smaller number of hidden units than the SLFNs trained by the original ELM and BP, which results in quick response to new input patterns.

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DO - 10.1109/FBIT.2007.30

M3 - Conference paper

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SN - 0769529992

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T3 - Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007

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BT - Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007

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ER -

Huynh HT, Kim JJ, Won Y. DNA microarray classification with compact single hidden-layer feedForward neural networks. In Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007. 2007. p. 193-198. 4524103. (Proceedings of the Frontiers in the Convergence of Bioscience and Information Technologies, FBIT 2007). doi: 10.1109/FBIT.2007.30

DNA microarray classification with compact single hidden-layer feedForward neural networks

Abstract

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UN SDGs

Quacquarelli Symonds(QS) Subject Topics

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